Facial expression recognition based on dual-feature fusion and improved random forest classifier

Facial expression recognition (FER) is an important means for machines to understand the changes in the facial expression of human beings. Expression recognition using single-modal facial images, such as gray scale, may suffer from illumination changes and the lack of detailed expression-related information. In this study, multi-modal facial images, such as facial gray scale, depth, and local binary pattern (LBP), are used to recognize six basic facial expressions, namely, happiness, sadness, anger, disgust, fear, and surprise. Facial depth images are used for robust face detection initially. The deep geometric feature is represented by point displacement and angle variation in facial landmark points with the help of depth information. The local appearance feature, which is obtained by concatenating LBP histograms of expression-prominent patches, is utilized to recognize those expression changes that are difficult to capture by only the geometric changes. Thereafter, an improved random forest classifier based on feature selection is used to recognize different facial expressions. Results of comparative evaluations in benchmarking datasets show that the proposed method outperforms several state-of-the-art FER approaches that are based on hand-crafted features. The capability of the proposed method is comparable to that of the popular convolutional neural-network-based FER approach but with fewer demands for training data and a high-performance hardware platform.     

Mutual information-based method for selecting informative feature sets

Feature selection is one of the fundamental problems in pattern recognition and data mining. A popular and effective approach to feature selection is based on information theory, namely the mutual information of features and class variable. In this paper we compare eight different mutual information-based feature selection methods. Based on the analysis of the comparison results, we propose a new mutual information-based feature selection method. By taking into account both the class-dependent and class-independent correlation among features, the proposed method selects a less redundant and more informative set of features. The advantage of the proposed method over other methods is demonstrated by the results of experiments on UCI datasets (Asuncion and Newman, 2010 [1]) and object recognition.     

一种可最优化计算特征规模的互信息特征提取

利用矩阵特征向量分解，提出一种可最优化计算特征规模的互信息特征提取方法．首先，论述了高斯分布假设下的该互信息判据的类可分特性，并证明了现有典型算法都是本算法的特例；然后，在给出该互信息判据严格的数学意义基础上，提出了基于矩阵特征向量分解计算最优化特征规模算法；最后，通过实际数据验证了该方法的有效性     

Subspace learning for unsupervised feature selection via matrix factorization

Dimensionality reduction is an important and challenging task in machine learning and data mining. Feature selection and feature extraction are two commonly used techniques for decreasing dimensionality of the data and increasing efficiency of learning algorithms. Specifically, feature selection realized in the absence of class labels, namely unsupervised feature selection, is challenging and interesting. In this paper, we propose a new unsupervised feature selection criterion developed from the viewpoint of subspace learning, which is treated as a matrix factorization problem. The advantages of this work are four-fold. First, dwelling on the technique of matrix factorization, a unified framework is established for feature selection, feature extraction and clustering. Second, an iterative update algorithm is provided via matrix factorization, which is an efficient technique to deal with high-dimensional data. Third, an effective method for feature selection with numeric data is put forward, instead of drawing support from the discretization process. Fourth, this new criterion provides a sound foundation for embedding kernel tricks into feature selection. With this regard, an algorithm based on kernel methods is also proposed. The algorithms are compared with four state-of-the-art feature selection methods using six publicly available datasets. Experimental results demonstrate that in terms of clustering results, the proposed two algorithms come with better performance than the others for almost all datasets we experimented with here.     

Deep peak-neutral difference feature for facial expression recognition

Facial expression recognition (FER) is important in vision-related applications. Deep neural networks demonstrate impressive performance for face recognition; however, it should be noted that this method relies heavily on a great deal of manually labeled training data, which is not available for facial expressions in real-world applications. Hence, we propose a powerful facial feature called deep peak–neutral difference (DPND) for FER. DPND is defined as the difference between two deep representations of the fully expressive (peak) and neutral facial expression frames. The difference tends to emphasize the facial parts that are changed in the transition from the neutral to the expressive face and to eliminate the face identity information retained in the fine-tuned deep neural network for facial expression, the network has been trained on large-scale face recognition dataset. Furthermore, unsupervised clustering and semi-supervised classification methods are presented to automatically acquire the neutral and peak frames from the expression sequence. The proposed facial expression feature achieved encouraging results on public databases, which suggests that it has strong potential to recognize facial expressions in real-world applications.

     

MIFS-ND: A mutual information-based feature selection method

Feature selection is used to choose a subset of relevant features for effective classification of data. In high dimensional data classification, the performance of a classifier often depends on the feature subset used for classification. In this paper, we introduce a greedy feature selection method using mutual information. This method combines both feature–feature mutual information and feature–class mutual information to find an optimal subset of features to minimize redundancy and to maximize relevance among features. The effectiveness of the selected feature subset is evaluated using multiple classifiers on multiple datasets. The performance of our method both in terms of classification accuracy and execution time performance, has been found significantly high for twelve real-life datasets of varied dimensionality and number of instances when compared with several competing feature selection techniques.     

基于专家特征的条件互信息多标记特征选择算法

特征选择对于分类器的分类精度和泛化性能起重要作用。目前的多标记特征选择算法主要利用最大相关性最小冗余性准则在全部特征集中进行特征选择,没有考虑专家特征,因此多标记特征选择算法的运行时间较长、复杂度较高。实际上,在现实生活中专家依据几个或者多个关键特征就能够直接决定整体的预测方向。如果提取关注这些信息,必将减少特征选择的计算时间,甚至提升分类器性能。基于此,提出一种基于专家特征的条件互信息多标记特征选择算法。首先将专家特征与剩余的特征相联合,再利用条件互信息得出一个与标记集合相关性由强到弱的特征序列,最后通过划分子空间去除冗余性较大的特征。该算法在7个多标记数据集上进行了实验对比,结果表明该算法较其他特征选择算法有一定优势,统计假设检验与稳定性分析进一步证明了所提出算法的有效性和合理性。     

A novel feature selection method based on normalized mutual information

In this paper, a novel feature selection method based on the normalization of the well-known mutual information measurement is presented. Our method is derived from an existing approach, the max-relevance and min-redundancy (mRMR) approach. We, however, propose to normalize the mutual information used in the method so that the domination of the relevance or of the redundancy can be eliminated. We borrow some commonly used recognition models including Support Vector Machine (SVM), k-Nearest-Neighbor (kNN), and Linear Discriminant Analysis (LDA) to compare our algorithm with the original (mRMR) and a recently improved version of the mRMR, the Normalized Mutual Information Feature Selection (NMIFS) algorithm. To avoid data-specific statements, we conduct our classification experiments using various datasets from the UCI machine learning repository. The results confirm that our feature selection method is more robust than the others with regard to classification accuracy.     

A parallel feature selection method study for text classification

Text classification is a popular research topic in data mining. Many classification methods have been proposed. Feature selection is an important technique for text classification since it is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. In recent years, data have become increasingly larger in both the number of instances and the number of features in many applications. As a result, classical feature selection methods do not work well in processing large-scale dataset due to the expensive computational cost. To address this issue, in this paper, a parallel feature selection method based on MapReduce is proposed. Specifically, mutual information based on Renyi entropy is used to measure the relationship between feature variables and class variables. Maximum mutual information theory is then employed to choose the most informative combination of feature variables. We implemented the selection process based on MapReduce, which is efficient and scalable for large-scale problems. At last, a practical example well demonstrates the efficiency of the proposed method.

     

基于改进轻量级秩扩展网络的人脸表情识别方法

人脸表情识别作为人机交互的一种重要方法,广泛应用于智能医疗、公安测谎系统、车载安全系统等领域。现有人脸表情识别方法多数存在参数量冗余、计算成本高、特征表达瓶颈等问题。提出一种基于改进轻量级秩扩展网络ReXNet的人脸表情识别方法。通过构建改进的ReXNet以提取人脸表情特征,在参数量较少的条件下解决特征表达瓶颈的问题,增强对表情局部特征的关注,获得高层次的表情特征,同时融合坐标注意力模块,将位置信息嵌入到通道注意力中,精准地定位和识别感兴趣的特征,建立位置信息与局部特征之间的长依赖关系,减少计算开销。在此基础上,将细化模块引入到改进的网络架构中,利用类别上下文信息细化分类结果,增强类间的分化效果,从而提高人脸表情识别的准确率。实验结果表明,该方法在RAF-DB和FERPlus数据集上的人脸表情识别准确率分别达到88.43%和88.8%,相比VGG16-PLD、SHCNN、ResNet+VGG等方法,具有较高的准确率和较优的鲁棒性。     

最小冗余最大分离准则特征选择方法

特征选择是处理高维数据的一项有效技术。针对传统方法的不足,结合[F-score]与互信息,提出了一种最小冗余最大分离的特征选择评价准则,该准则使所选择的特征具有更好的分类和预测能力;采用二进制布谷鸟搜索算法和二次规划两种搜索策略来搜索最优特征子集,并对两种搜索策略的准确性和计算量进行分析比较;最后,利用UCI数据集进行实验测试,实验结果说明了所提理论的有效性。     

An adaptive heuristic for feature selection based on complementarity

Feature selection is a dimensionality reduction technique that helps to improve data visualization, simplify learning, and enhance the efficiency of learning algorithms. The existing redundancy-based approach, which relies on relevance and redundancy criteria, does not account for feature complementarity. Complementarity implies information synergy, in which additional class information becomes available due to feature interaction. We propose a novel filter-based approach to feature selection that explicitly characterizes and uses feature complementarity in the search process. Using theories from multi-objective optimization, the proposed heuristic penalizes redundancy and rewards complementarity, thus improving over the redundancy-based approach that penalizes all feature dependencies. Our proposed heuristic uses an adaptive cost function that uses redundancy–complementarity ratio to automatically update the trade-off rule between relevance, redundancy, and complementarity. We show that this adaptive approach outperforms many existing feature selection methods using benchmark datasets.     

A multi-objective feature selection method based on bacterial foraging optimization

Feature selection plays an important role in data preprocessing. The aim of feature selection is to recognize and remove redundant or irrelevant features. The key issue is to use as few features as possible to achieve the lowest classification error rate. This paper formulates feature selection as a multi-objective problem. In order to address feature selection problem, this paper uses the multi-objective bacterial foraging optimization algorithm to select the feature subsets and k-nearest neighbor algorithm as the evaluation algorithm. The wheel roulette mechanism is further introduced to remove duplicated features. Four information exchange mechanisms are integrated into the bacteria-inspired algorithm to avoid the individuals getting trapped into the local optima so as to achieve better results in solving high-dimensional feature selection problem. On six small datasets and ten high-dimensional datasets, comparative experiments with different conventional wrapper methods and several evolutionary algorithms demonstrate the superiority of the proposed bacteria-inspired based feature selection method.

     

Maximization of mutual information for supervised linear feature extraction.

In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.     

基于自适应注意力融合特征提取网络的图像超分辨率

为了应对当前大型图像超分辨率模型参数过多难以部署,以及现有的轻量级图像超分辨率模型性能表现不佳的问题,提出了一种基于自适应注意力融合特征提取网络的图像超分辨率模型。该模型主要由一个大核注意力模块和多个高效注意力融合特征提取模块组成。首先,利用大核注意力模块进行浅层特征提取,然后将提取到的浅层特征信息输入级联的高效注意力融合特征提取模块进行深层特征提取、增强、细化和再分配的聚合操作。高效注意力融合特征提取模块由三个部分组成,分别是渐进式残差特征提取模块、通道对比度感知注意力模块和通道—空间联合注意力模块。该网络可以在利用少量参数的情况下实现更好的图像超分辨率性能,是一种表现优异的轻量级图像超分辨率模型。通过在流行的基准数据集上评估提出的方法,并与现有的一些方法进行对比,结果表明该方法的表现更优异。     

Facial expression recognition using iterative universum twin support vector machine

Facial expressions are one of the most important characteristics of human behaviour. They are very useful in applications on human computer interaction. To classify facial emotions, different feature extraction methods are used with machine learning techniques. In supervised learning, information about the distribution of data is given by data points not belonging to any of the classes. These data points are known as universum data. In this work, we use universum data to perform multiclass classification of facial emotions from human facial images. Moreover, the existing universum based models suffer from the drawback of high training cost, so we propose an iterative universum twin support vector machine (IUTWSVM) using Newton method. Our IUTWSVM gives good generalization performance with less computation cost. To solve the optimization problem of proposed IUTWSVM, no optimization toolbox is required. Further, improper selection of universum points always leads to degraded performance of the model. For generating better universum, a novel scheme is proposed in this work based on information entropy of data. To check the effectiveness of proposed IUTWSVM, several numerical experiments are performed on benchmark real world datasets. For multiclass classification of facial emotions, the performance of IUTWSVM is compared with existing algorithms using different feature extraction techniques. Our proposed algorithm shows better generalization performance with less training cost in both binary as well as multiclass classification problems.     

Generic form feature recognition and operation selection using connectionist modelling

For the purpose of enhancing the adaptability of computer-aided process planning systems, two connectionist modelling methods, namely neocognitron (i.e. neural network modelling for pattern recognition) and fuzzy associative memories (FAM), are applied to the phases of feature recognition and operation selection respectively in order to provide the system with the ability of self-learning and the ability to integrate traditional expert planning systems with connectionism-based models. In this paper, the attributed adjacency graph (AAG) extracted from a (B-Rep) solid model is converted to attributed adjacency matrices (AAM) that can be used as input data for the neocognitron to train and recognize feature patterns. With this technique, the system can not only self-reconstruct its recognition abilities for new features by learning without a priori knowledge but can also recognize and decompose intersection features. A fuzzy connectionist model, which is created using the Hebbian fuzzy learning algorithm, is employed subsequently to map the features to the appropriate operations. As the algorithm is capable of learning from rules, it is much easier to integrate the proposed model with conventional expert CAPP systems so that they become more generic in dealing with uncertain information processing and perform knowledge updating. mg]Keywords mw]Computer-aided process planning mw]feature recognition mw]neural networks mw]fuzzy neural networks mw]operation selection mw]connectionist model mw]fuzzy associative memories     

人脸表情识别研究的新进展

人脸表情识别（facial expression recognition,简称FER）作为智能化人机交互技术中的一个重要组成部分,近年来得到了广泛的关注,涌现出许多新方法.本文综述了国内外近4年人脸表情识别（FER）技术的最新发展.首先,介绍了FER系统的组成：人脸检测、表情特征提取和表情分类,并详细叙述了其中表情特征提取和表情分类的方法.然后,对目前广泛应用的人脸表情数据库进行了介绍,并在此基础上对当前一些FER系统的性能进行了比较分析.最后,对FER领域的研究现状和挑战给予了评述,对FER可能的发展方向进行了讨论.     

Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information

A novel feature selection method using the concept of mutual information (MI) is proposed in this paper. In all MI based feature selection methods, effective and efficient estimation of high-dimensional MI is crucial. In this paper, a pruned Parzen window estimator and the quadratic mutual information (QMI) are combined to address this problem. The results show that the proposed approach can estimate the MI in an effective and efficient way. With this contribution, a novel feature selection method is developed to identify the salient features one by one. Also, the appropriate feature subsets for classification can be reliably estimated. The proposed methodology is thoroughly tested in four different classification applications in which the number of features ranged from less than 10 to over 15000. The presented results are very promising and corroborate the contribution of the proposed feature selection methodology.     

深度人脸表情识别研究进展

随着人脸表情识别任务逐渐从实验室受控环境转移至具有挑战性的真实世界环境,在深度学习技术的迅猛发展下,深度神经网络能够学习出具有判别能力的特征,逐渐应用于自动人脸表情识别任务。目前的深度人脸表情识别系统致力于解决以下两个问题：1）由于缺乏足量训练数据导致的过拟合问题;2）真实世界环境下其他与表情无关因素变量（例如光照、头部姿态和身份特征）带来的干扰问题。本文首先对近十年深度人脸表情识别方法的研究现状以及相关人脸表情数据库的发展进行概括。然后,将目前基于深度学习的人脸表情识别方法分为两类：静态人脸表情识别和动态人脸表情识别,并对这两类方法分别进行介绍和综述。针对目前领域内先进的深度表情识别算法,对其在常见表情数据库上的性能进行了对比并详细分析了各类算法的优缺点。最后本文对该领域的未来研究方向和机遇挑战进行了总结和展望：考虑到表情本质上是面部肌肉运动的动态活动,基于动态序列的深度表情识别网络往往能够取得比静态表情识别网络更好的识别效果。此外,结合其他表情模型如面部动作单元模型以及其他多媒体模态,如音频模态和人体生理信息能够将表情识别拓展到更具有实际应用价值的场景。